Computer-aided craniofacial superimposition using a quasi-Newton iterative closest point approach

Joi San Tana,*, Ibrahim Venkata, Paul T. Jayaprakashb

ABSTRACT: Craniofacial superimposition is a forensic imaging technique used to identify an unknown skull by matching it with the available face photographs of missing individuals. Life-size enlargement of the face image and orientating the skull to correspond to the posture seen in the face photograph are the two main problems that exist in conventional as well as in the computer-aided craniofacial superimposition. Here we address these two potential issues by proposing a novel computer-aided approach which uses the quasi-Newton optimization method and iterative closest point algorithm. The results showed that the quasi-Newton method proposed is able to eliminate 76% and 66% of false matches, respectively, for the male and the female skull superimpositions during the initial stage of filtration. Our experimental results demonstrate that the proposed approach is efficient in assigning face photographs as inclusions (positives) and exclusions (negatives) while superimposing with related and unrelated skulls.